Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-28T09:24:15.207Z Has data issue: false hasContentIssue false
  • English
  • Français

Effect of purple (Cyperus rotundus) and yellow nutsedge (C. esculentus) on growth and reflectance characteristics of cotton and soybean

Published online by Cambridge University Press:  20 January 2017

Chris T. Leon ,
David R. Shaw ,
Lori M. Bruce  and
Clarence Watson
Chris T. Leon
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Lori M. Bruce
Affiliation:
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762
Clarence Watson
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Get access Rights & Permissions [Opens in a new window]

Abstract

Because of interest in monitoring crop response to weed interference, greenhouse experiments were conducted to evaluate interference of purple and yellow nutsedge on the growth, development, and spectral response of cotton and soybean. Cotton fresh weight was reduced 9 to 42% compared with the control when grown with yellow and purple nutsedge. Fresh weight of soybean was reduced 27 to 60% when it emerged simultaneously with yellow nutsedge and 45 to 63% when it emerged 7 d after yellow nutsedge. Soybean fresh weight was reduced 30 to 35% when it emerged simultaneously with purple nutsedge and 44 to 72% when it emerged 7 d after purple nutsedge. Reflectance data were analyzed using wavelet transformation techniques with the HAAR mother wavelet. Nine extracted features from the cotton and soybean leaf reflectance measurements were used to classify single-leaf cotton and soybean reflectance measurements to predict whether cotton or soybean was growing in the presence or absence of purple and yellow nutsedge. After training the system, the ability to separate leaf reflectance measurements of crops growing weed free from those growing in the presence of purple and yellow nutsedge was tested using cross-validation with the nearest mean classifier. Cross-validation accuracy results for cotton were 62 to 70%. Cross-validation accuracy for soybean and yellow nutsedge was similar, regardless of emergence, and ranged from 60 to 71%. Features extracted from the soybean reflectance measurements were not as effective in classifying soybean leaf reflectance measurements based on the presence or absence of purple nutsedge. A decrease in accuracy was observed for both simultaneous and delayed soybean emergence in purple nutsedge fresh weight categories from more than 2,560 g to more than 3,420 g. Overall, the system correctly classified soybean emerging simultaneously with purple nutsedge 58 to 74% and soybean emerging 7 d after purple nutsedge 53 to 67%. These results indicate the potential of differentiating crops under stress using spectral reflectance, although refinements to the system must be made before it is field ready.

Keywords

Yellow nutsedge, Cyperus esculentus L. CYPESpurple nutsedge, Cyperus rotundus L. CYPROsoybean, Glycine max (L.) Merr.cotton, Gossypium hirsutum L.HAARhyperspectralinterferencewavelet transformation
Type
Research Article
Information
Weed Science , Volume 51 , Issue 4 , August 2003 , pp. 557 - 564
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Black, C. C., Chen, T. M., and Brown, R. H. 1969. Biochemical basis for plant competition. Weed Sci. 17:338–344.CrossRefGoogle Scholar
Bruce, L. M. and Li, J. 1999. Fast wavelet-based algorithms for multiresolutional decomposition and feature extraction of hyperspectral signatures. Proc. SPIE 3717:72–81.CrossRefGoogle Scholar
Chivinge, O. A. 1985. Allelopathic effects of purple nutsedge (Cyperus rotundus L.) on the growth and development of cotton, maize, and soybeans. Zimb. Agric. J. 82:151–152.Google Scholar
Drost, D. C. and Doll, J. D. 1980. The allelopathic effect of yellow nutsedge (Cyperus esculentus) on corn (Zea mays) and soybeans (Glycine max). Weed Sci. 28:229–233.CrossRefGoogle Scholar
Elmore, C. D., Brown, M. A., and Flint, E. P. 1983. Early interference between cotton (Gossypium hirsutum) and four weed species. Weed Sci. 31:200–207.CrossRefGoogle Scholar
Fisher, R. A. 1936. The statistical utilization of multiple measurements. Ann. Eugen. 7:179–188.CrossRefGoogle Scholar
Friedman, T. and Horowitz, M. 1971. Biologically active substances in subterranean parts of purple nutsedge. Weed Sci. 19:398–401.CrossRefGoogle Scholar
Hanley, J. A. and McNeil, B. J. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Diagn. Radiol. 143:29–36.Google Scholar
Horowitz, M. 1973. Competitive effects of Cynodon dactylon, Sorghum halepense, and Cyperus rotundus on cotton and mustard. Exp. Agric. 9:263–273.CrossRefGoogle Scholar
Jackson, R. D. 1986. Remote sensing of biotic and abiotic plant stress. Ann. Rev. Phytopathol. 24:265–287.CrossRefGoogle Scholar
Johnson, D. E. 1998. Discriminant analysis. Pages 217–285 In Applied Multivariate Methods for Data Analysts. Pacific Grove, CA: Duxbury.Google Scholar
Keeley, P. E. and Thullen, R. J. 1975. Influence of yellow nutsedge competition on furrow-irrigated cotton. Weed Sci. 23:171–175.CrossRefGoogle Scholar
Keeley, P. E. and Thullen, R. J. 1983. Influence of yellow nutsedge (Cyperus esculentus)—free periods on yield of cotton (Gossypium hirsutum). Weed Sci. 31:803–807.CrossRefGoogle Scholar
Mallat, S. 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Patt. Anal. Machine Intell. 11:674–693.CrossRefGoogle Scholar
Moffett, J. E. and McCloskey, W. B. 1998. Effects of soil moisture and yellow nutsedge (Cyperus esculentus) density on cotton (Gossypium hirsutum). Weed Sci. 46:231–237.CrossRefGoogle Scholar
Patterson, M. G., Buchanan, G. A., Street, J. E., and Crowley, R. H. 1980. Yellow nutsedge (Cyperus esculentus) competition with cotton (Gossypium hirsutum). Weed Sci. 28:327–329.CrossRefGoogle Scholar
Penuelas, J. and Filella, I. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci. 3:151–155.CrossRefGoogle Scholar
Radford, A. E., Ahles, H. E., and Bell, C. R. 1968. Cyperus rotundus and Cyperus esculentus . Page 174 In Manual of the Vascular Flora of the Carolinas. Chapel Hill, NC: The University of North Carolina Press.Google Scholar
Reynolds, D. B., ed. 2000. Weed survey—Southern States. Broadleaf crops subsection. Proc. South. Weed Sci. Soc. 54:244–259.Google Scholar
Stork, C. L., Veltkamp, D. J., and Kowalski, B. R. 1998. Detecting and identifying spectral anomalies using wavelet processing. Appl. Spectrosc. 52:1348–1352.CrossRefGoogle Scholar
Thenkabail, P. S., Smith, R. B., and Pauw, E. D. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 71:158–182.CrossRefGoogle Scholar